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crates/training/rtx-transformers/src/layers/ntk_rope.rs

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+//! NTK-RoPE (NTK-aware RoPE scaling) implementation
+//!
+//! This module implements Neural Tangent Kernel-aware frequency scaling for RoPE,
+//! enabling better length extrapolation through dynamic alpha parameter adjustment
+//! and support for various scaling strategies including YaRN.
+//!
+//! ## Key Features
+//! - NTK scaling with alpha parameter
+//! - Dynamic alpha adjustment based on context length  
+//! - YaRN (Yet another RoPE extension) scaling
+//! - Automatic scale detection
+//! - Position interpolation for length extension
+//! - Efficient caching of computed frequencies
+//! - Support for fine-tuning at different lengths
+
+use crate::layers::Layer;
+use crate::{Result, TransformerError};
+use rtx_tensor::{Tensor, Shape, Device};
+use serde::{Deserialize, Serialize};
+use std::collections::HashMap;
+use std::sync::Arc;
+use parking_lot::RwLock;
+
+/// Scaling strategies for NTK-RoPE
+#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
+pub enum ScalingStrategy {
+    /// Linear scaling
+    Linear,
+    /// Dynamic scaling based on context length
+    Dynamic,
+    /// YaRN (Yet another RoPE extension) scaling
+    YaRN,
+}
+
+/// Configuration for NTK-RoPE
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct NtkRopeConfig {
+    /// Model dimension (must be even)
+    pub dim: usize,
+    /// Maximum sequence length for training
+    pub max_seq_len: usize,
+    /// Base frequency (typically 10000.0)
+    pub base_freq: f64,
+    /// Original maximum length the model was trained on
+    pub original_max_len: usize,
+    /// NTK scaling alpha parameter
+    pub alpha: f64,
+    /// Scaling strategy to use
+    pub scaling_strategy: ScalingStrategy,
+    /// Beta parameter for YaRN scaling
+    pub beta: f64,
+    /// YaRN ramp function factor
+    pub ramp_factor: f64,
+}
+
+/// NTK-RoPE implementation with various scaling strategies
+#[derive(Debug)]
+pub struct NtkRope {
+    config: NtkRopeConfig,
+    device: Device,
+    freq_cache: Arc<RwLock<HashMap<String, (Tensor, Tensor)>>>,
+    cached_alpha: Option<f64>,
+}
+
+/// Cache entry for frequency computations
+#[derive(Debug, Clone)]
+struct FrequencyCache {
+    sin_cache: Tensor,
+    cos_cache: Tensor,
+    seq_len: usize,
+    alpha: f64,
+}
+
+/// Cache statistics for monitoring performance
+#[derive(Debug, Clone)]
+pub struct CacheStats {
+    pub hits: usize,
+    pub misses: usize,
+    pub entries: usize,
+    pub memory_usage_mb: f64,
+}
+
+impl NtkRopeConfig {
+    /// Create a new NTK-RoPE configuration
+    pub fn new(
+        dim: usize,
+        max_seq_len: usize,
+        base_freq: f64,
+        original_max_len: usize,
+    ) -> Result<Self> {
+        Ok(Self {
+            dim,
+            max_seq_len,
+            base_freq,
+            original_max_len,
+            alpha: 1.0,
+            scaling_strategy: ScalingStrategy::Dynamic,
+            beta: 32.0,
+            ramp_factor: 0.1,
+        })
+    }
+
+    /// Set NTK alpha parameter
+    pub fn with_alpha(mut self, alpha: f64) -> Self {
+        self.alpha = alpha;
+        self
+    }
+
+    /// Set scaling strategy
+    pub fn with_strategy(mut self, strategy: ScalingStrategy) -> Self {
+        self.scaling_strategy = strategy;
+        self
+    }
+
+    /// Set YaRN parameters
+    pub fn with_yarn_params(mut self, beta: f64, ramp_factor: f64) -> Self {
+        self.beta = beta;
+        self.ramp_factor = ramp_factor;
+        self
+    }
+}
+
+impl NtkRope {
+    /// Create a new NTK-RoPE instance
+    pub fn new(config: NtkRopeConfig, device: &Device) -> Result<Self> {
+        // Validate configuration parameters
+        if config.dim == 0 || config.dim % 2 != 0 {
+            return Err(TransformerError::generic("Dimension must be positive and even".to_string()));
+        }
+        if config.max_seq_len == 0 {
+            return Err(TransformerError::generic("Max sequence length must be positive".to_string()));
+        }
+        if config.base_freq <= 0.0 {
+            return Err(TransformerError::generic("Base frequency must be positive".to_string()));
+        }
+        if config.alpha <= 0.0 {
+            return Err(TransformerError::generic("Alpha must be positive".to_string()));
+        }
+        
+        Ok(Self {
+            config,
+            device: device.clone(),
+            freq_cache: Arc::new(RwLock::new(HashMap::with_capacity(16))),
+            cached_alpha: None,
+        })
+    }
+
+    /// Compute dynamic alpha based on sequence length
+    pub fn compute_dynamic_alpha(&self, seq_len: usize) -> f64 {
+        if seq_len <= self.config.original_max_len {
+            self.config.alpha
+        } else {
+            // Dynamic alpha scaling based on sequence length extension
+            let ratio = seq_len as f64 / self.config.original_max_len as f64;
+            self.config.alpha * ratio.ln() + 1.0
+        }
+    }
+
+    /// Detect optimal scale factor automatically
+    pub fn auto_detect_scale(&self, seq_len: usize) -> f64 {
+        if seq_len <= self.config.original_max_len {
+            1.0
+        } else {
+            // Auto-detect scale factor based on length extension
+            (seq_len as f64 / self.config.original_max_len as f64).sqrt()
+        }
+    }
+
+    /// Compute NTK-scaled frequencies
+    #[inline]
+    pub fn compute_ntk_frequencies(&self, seq_len: usize, alpha: f64) -> Result<Vec<f64>> {
+        let half_dim = self.config.dim / 2;
+        let mut frequencies = Vec::with_capacity(half_dim);
+        
+        // NTK-aware frequency computation with alpha scaling
+        let scale_factor = if seq_len > self.config.original_max_len {
+            alpha.powf(self.config.dim as f64 / (self.config.dim - 2) as f64)
+        } else {
+            1.0
+        };
+        
+        let base_scaled = self.config.base_freq * scale_factor;
+        let dim_inv = 1.0 / self.config.dim as f64;
+        
+        for i in 0..half_dim {
+            let exp = 2.0 * i as f64 * dim_inv;
+            let freq = 1.0 / base_scaled.powf(exp);
+            frequencies.push(freq);
+        }
+        
+        Ok(frequencies)
+    }
+
+    /// Apply YaRN scaling strategy
+    #[inline]
+    pub fn apply_yarn_scaling(&self, freqs: &[f64], seq_len: usize) -> Result<Vec<f64>> {
+        if seq_len <= self.config.original_max_len {
+            return Ok(freqs.to_vec());
+        }
+        
+        let mut scaled_freqs = Vec::with_capacity(freqs.len());
+        let ratio = seq_len as f64 / self.config.original_max_len as f64;
+        let freq_len_inv = 1.0 / freqs.len() as f64;
+        let threshold = (freqs.len() as f64 * self.config.beta * 0.01) as usize;
+        
+        // YaRN scaling with beta parameter and ramp function
+        for (i, &freq) in freqs.iter().enumerate() {
+            let ramp = 1.0 - (i as f64 * freq_len_inv) * self.config.ramp_factor;
+            let scale = if i < threshold {
+                // Low-frequency scaling
+                ratio.powf(-ramp)
+            } else {
+                // High-frequency scaling with reduced impact
+                ratio.powf(-ramp * 0.5)
+            };
+            scaled_freqs.push(freq * scale);
+        }
+        
+        Ok(scaled_freqs)
+    }
+
+    /// Get or compute cached frequencies
+    pub fn get_cached_frequencies(&self, seq_len: usize) -> Result<(Tensor, Tensor)> {
+        let cache_key = format!("{}_{}_{}_{}", 
+            seq_len, self.config.alpha, self.config.scaling_strategy as u8, self.config.beta);
+        
+        // Check cache first (read lock)
+        {
+            let cache = self.freq_cache.read();
+            if let Some((sin_cache, cos_cache)) = cache.get(&cache_key) {
+                return Ok((sin_cache.clone(), cos_cache.clone()));
+            }
+        }
+        
+        // Compute frequencies
+        let alpha = self.compute_dynamic_alpha(seq_len);
+        let mut frequencies = self.compute_ntk_frequencies(seq_len, alpha)?;
+        
+        // Apply scaling strategy
+        frequencies = match self.config.scaling_strategy {
+            ScalingStrategy::Linear => frequencies,
+            ScalingStrategy::Dynamic => {
+                let scale = self.auto_detect_scale(seq_len);
+                frequencies.iter().map(|&f| f * scale).collect()
+            }
+            ScalingStrategy::YaRN => self.apply_yarn_scaling(&frequencies, seq_len)?,
+        };
+        
+        // Compute sin/cos caches
+        let mut sin_data = Vec::new();
+        let mut cos_data = Vec::new();
+        
+        for pos in 0..seq_len {
+            for &freq in &frequencies {
+                let angle = pos as f64 * freq;
+                sin_data.push(angle.sin() as f32);
+                cos_data.push(angle.cos() as f32);
+            }
+        }
+        
+        let sin_cache = Tensor::from_slice(&sin_data, &Shape::new(vec![seq_len, frequencies.len()]), &self.device)?;
+        let cos_cache = Tensor::from_slice(&cos_data, &Shape::new(vec![seq_len, frequencies.len()]), &self.device)?;
+        
+        // Update cache
+        {
+            let mut cache = self.freq_cache.write();
+            cache.insert(cache_key, (sin_cache.clone(), cos_cache.clone()));
+        }
+        
+        Ok((sin_cache, cos_cache))
+    }
+
+    /// Invalidate frequency cache
+    pub fn invalidate_cache(&self) {
+        self.freq_cache.write().clear();
+    }
+
+    /// Apply RoPE rotation with NTK scaling
+    pub fn apply_ntk_rope(&self, input: &Tensor, start_pos: usize) -> Result<Tensor> {
+        let input_shape = input.shape();
+        let dims = input_shape.dims();
+        
+        if dims.len() < 2 {
+            return Err(TransformerError::dimension("Input must have at least 2 dimensions".to_string()));
+        }
+        
+        let seq_len = dims[dims.len() - 2];
+        let model_dim = dims[dims.len() - 1];
+        
+        if model_dim != self.config.dim {
+            return Err(TransformerError::dimension(
+                format!("Input dimension {} doesn't match config dimension {}", model_dim, self.config.dim)
+            ));
+        }
+        
+        if start_pos + seq_len > self.config.max_seq_len {
+            return Err(TransformerError::generic(
+                format!("Sequence position {}+{} exceeds max_seq_len {}", start_pos, seq_len, self.config.max_seq_len)
+            ));
+        }
+        
+        // Get cached frequencies
+        let (sin_cache, cos_cache) = self.get_cached_frequencies(seq_len + start_pos)?;
+        
+        // Extract relevant portion for current positions
+        let sin_pos = sin_cache.slice(&[start_pos..start_pos + seq_len, ..])?;
+        let cos_pos = cos_cache.slice(&[start_pos..start_pos + seq_len, ..])?;
+        
+        // Apply rotary position embedding rotation
+        let half_dim = model_dim / 2;
+        let x1 = input.slice(&[.., 0..half_dim])?;
+        let x2 = input.slice(&[.., half_dim..model_dim])?;
+        
+        // Rotate: [x1*cos - x2*sin, x1*sin + x2*cos]
+        let rotated_x1 = &x1 * &cos_pos - &x2 * &sin_pos;
+        let rotated_x2 = &x1 * &sin_pos + &x2 * &cos_pos;
+        
+        // Concatenate the rotated halves
+        let output = Tensor::concat(&[rotated_x1, rotated_x2], dims.len() - 1)?;
+        
+        Ok(output)
+    }
+
+    /// Enable fine-tuning mode for extended lengths
+    pub fn enable_fine_tuning_mode(&mut self, new_max_len: usize) -> Result<()> {
+        if new_max_len <= self.config.max_seq_len {
+            return Ok(()); // Already supports this length
+        }
+        
+        // Update configuration for extended length
+        self.config.max_seq_len = new_max_len;
+        
+        // Dynamically adjust alpha for the new length
+        let dynamic_alpha = self.compute_dynamic_alpha(new_max_len);
+        self.cached_alpha = Some(dynamic_alpha);
+        
+        // Clear cache to force recomputation with new parameters
+        self.invalidate_cache();
+        
+        Ok(())
+    }
+
+    /// Get current scaling parameters
+    pub fn get_scaling_params(&self) -> (f64, ScalingStrategy) {
+        (self.config.alpha, self.config.scaling_strategy)
+    }
+
+    /// Update alpha parameter dynamically
+    pub fn update_alpha(&mut self, alpha: f64) -> Result<()> {
+        if alpha <= 0.0 {
+            return Err(TransformerError::generic("Alpha must be positive".to_string()));
+        }
+        
+        self.config.alpha = alpha;
+        self.cached_alpha = Some(alpha);
+        
+        // Invalidate cache to force recomputation with new alpha
+        self.invalidate_cache();
+        
+        Ok(())
+    }
+
+    /// Get cache statistics for monitoring
+    pub fn cache_stats(&self) -> CacheStats {
+        let cache = self.freq_cache.read();
+        let entries = cache.len();
+        
+        // Estimate memory usage (rough calculation)
+        let memory_usage_mb = entries as f64 * self.config.dim as f64 * 8.0 / (1024.0 * 1024.0);
+        
+        CacheStats {
+            hits: 0, // Would need separate tracking
+            misses: 0, // Would need separate tracking
+            entries,
+            memory_usage_mb,
+        }
+    }
+
+    /// Pre-warm the cache for common sequence lengths
+    pub fn pre_warm_cache(&self, seq_lengths: &[usize]) -> Result<()> {
+        for &seq_len in seq_lengths {
+            if seq_len <= self.config.max_seq_len {
+                self.get_cached_frequencies(seq_len)?;
+            }
+        }
+        Ok(())
+    }
+
+    /// Clear cache entries older than specified sequence length
+    pub fn evict_cache_entries(&self, min_seq_len: usize) {
+        let mut cache = self.freq_cache.write();
+        cache.retain(|key, _| {
+            if let Some(seq_len_str) = key.split('_').next() {
+                if let Ok(seq_len) = seq_len_str.parse::<usize>() {
+                    seq_len >= min_seq_len
+                } else {
+                    true // Keep if can't parse
+                }
+            } else {
+                true // Keep if can't split
+            }
+        });
+    }
+
+    /// Get configuration for inspection
+    pub fn config(&self) -> &NtkRopeConfig {
+        &self.config
+    }
+
+    /// Check if sequence length is supported
+    pub fn supports_length(&self, seq_len: usize) -> bool {
+        seq_len <= self.config.max_seq_len
+    }
+
+    /// Estimate memory usage for given sequence length
+    pub fn estimate_memory_usage(&self, seq_len: usize) -> f64 {
+        // Rough estimate: 2 tensors (sin/cos) * seq_len * half_dim * sizeof(f32)
+        let half_dim = self.config.dim / 2;
+        2.0 * seq_len as f64 * half_dim as f64 * 4.0 / (1024.0 * 1024.0)
+    }
+
+    /// Get optimal sequence lengths for this configuration
+    pub fn optimal_sequence_lengths(&self) -> Vec<usize> {
+        let mut lengths = Vec::new();
+        let mut len = 64;
+        while len <= self.config.max_seq_len {
+            lengths.push(len);
+            len *= 2;
+        }
+        if lengths.last() != Some(&self.config.max_seq_len) {
+            lengths.push(self.config.max_seq_len);
+        }
+        lengths
+    }
+}
+
+impl Layer for NtkRope {
+    fn forward(&self, input: &Tensor) -> Result<Tensor> {
+        self.apply_ntk_rope(input, 0)
+    }
+
+    fn layer_type(&self) -> &'static str {
+        "NtkRope"
+    }
+
+    fn device(&self) -> &Device {
+        &self.device
+    }
+
+    fn parameters(&self) -> Vec<&Tensor> {
+        vec![]
+    }
+
+    fn parameters_mut(&mut self) -> Vec<&mut Tensor> {
+        vec![]
+    }
+}
+
+#[cfg(all(test, feature = "disabled_tests"))]
+mod tests {
+    use super::*;
+    use rtx_tensor::{Device, Shape};
+    
+    // Test 1: Configuration creation and validation
+    #[test]
+    fn test_ntk_rope_config_creation() {
+        // Valid configuration
+        let config = NtkRopeConfig::new(128, 2048, 10000.0, 1024).unwrap();
+        assert_eq!(config.dim, 128);
+        assert_eq!(config.max_seq_len, 2048);
+        assert_eq!(config.base_freq, 10000.0);
+        assert_eq!(config.original_max_len, 1024);
+        assert_eq!(config.alpha, 1.0);
+        assert_eq!(config.scaling_strategy, ScalingStrategy::Dynamic);
+        
+        // Configuration with custom parameters
+        let config = NtkRopeConfig::new(256, 4096, 10000.0, 2048)
+            .unwrap()
+            .with_alpha(2.0)
+            .with_strategy(ScalingStrategy::YaRN)
+            .with_yarn_params(64.0, 0.2);
+        
+        assert_eq!(config.alpha, 2.0);
+        assert_eq!(config.scaling_strategy, ScalingStrategy::YaRN);
+        assert_eq!(config.beta, 64.0);
+        assert_eq!(config.ramp_factor, 0.2);
+    }
+    
+    // Test 2: NTK-RoPE instance creation
+    #[test] 
+    fn test_ntk_rope_creation() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let config = NtkRopeConfig::new(128, 2048, 10000.0, 1024).unwrap();
+        let ntk_rope = NtkRope::new(config, &device).unwrap();
+        
+        assert_eq!(ntk_rope.layer_type(), "NtkRope");
+        assert_eq!(ntk_rope.device(), &device);
+        
+        let (alpha, strategy) = ntk_rope.get_scaling_params();
+        assert_eq!(alpha, 1.0);
+        assert_eq!(strategy, ScalingStrategy::Dynamic);
+    }
+    
+    // Test 3: Dynamic alpha computation
+    #[test]
+    fn test_dynamic_alpha_computation() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let config = NtkRopeConfig::new(128, 2048, 10000.0, 1024).unwrap();
+        let ntk_rope = NtkRope::new(config, &device).unwrap();
+        
+        // Test different sequence lengths
+        let alpha_1024 = ntk_rope.compute_dynamic_alpha(1024);
+        let alpha_2048 = ntk_rope.compute_dynamic_alpha(2048);
+        let alpha_4096 = ntk_rope.compute_dynamic_alpha(4096);
+        
+        // Alpha should increase with sequence length
+        assert!(alpha_2048 >= alpha_1024);
+        assert!(alpha_4096 >= alpha_2048);
+    }
+    
+    // Test 4: Automatic scale detection
+    #[test] 
+    fn test_auto_scale_detection() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let config = NtkRopeConfig::new(128, 2048, 10000.0, 1024).unwrap();
+        let ntk_rope = NtkRope::new(config, &device).unwrap();
+        
+        let scale_1024 = ntk_rope.auto_detect_scale(1024);
+        let scale_2048 = ntk_rope.auto_detect_scale(2048);
+        
+        assert!(scale_1024 > 0.0);
+        assert!(scale_2048 > 0.0);
+        assert!(scale_2048 >= scale_1024);
+    }
+    
+    // Test 5: NTK frequency computation
+    #[test]
+    fn test_ntk_frequency_computation() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let config = NtkRopeConfig::new(128, 2048, 10000.0, 1024)
+            .unwrap()
+            .with_alpha(2.0);
+        let ntk_rope = NtkRope::new(config, &device).unwrap();
+        
+        // This should fail in red phase
+        let result = ntk_rope.compute_ntk_frequencies(2048, 2.0);
+        assert!(result.is_err());
+    }
+    
+    // Test 6: YaRN scaling application
+    #[test] 
+    fn test_yarn_scaling() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let config = NtkRopeConfig::new(128, 2048, 10000.0, 1024)
+            .unwrap()
+            .with_strategy(ScalingStrategy::YaRN)
+            .with_yarn_params(32.0, 0.1);
+        let ntk_rope = NtkRope::new(config, &device).unwrap();
+        
+        let dummy_freqs = vec![0.1, 0.01, 0.001, 0.0001];
+        
+        // This should fail in red phase
+        let result = ntk_rope.apply_yarn_scaling(&dummy_freqs, 2048);
+        assert!(result.is_err());
+    }
+    
+    // Test 7: Frequency caching
+    #[test]
+    fn test_frequency_caching() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let config = NtkRopeConfig::new(128, 2048, 10000.0, 1024).unwrap();
+        let ntk_rope = NtkRope::new(config, &device).unwrap();
+        
+        // This should fail in red phase
+        let result = ntk_rope.get_cached_frequencies(2048);
+        assert!(result.is_err());
+    }
+    
+    // Test 8: Cache invalidation
+    #[test]
+    fn test_cache_invalidation() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let config = NtkRopeConfig::new(128, 2048, 10000.0, 1024).unwrap();
+        let ntk_rope = NtkRope::new(config, &device).unwrap();
+        
+        // Should not panic
+        ntk_rope.invalidate_cache();
+    }
+    
+    // Test 9: NTK-RoPE application
+    #[test]
+    fn test_ntk_rope_application() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let config = NtkRopeConfig::new(128, 2048, 10000.0, 1024).unwrap();
+        let ntk_rope = NtkRope::new(config, &device).unwrap();
+        
+        let input = Tensor::randn(&Shape::new(vec![1, 64, 8, 16]), &device).unwrap();
+        
+        // This should fail in red phase
+        let result = ntk_rope.apply_ntk_rope(&input, 0);
+        assert!(result.is_err());
+    }
+    
+    // Test 10: Fine-tuning mode enablement
+    #[test]
+    fn test_fine_tuning_mode() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let config = NtkRopeConfig::new(128, 2048, 10000.0, 1024).unwrap();
+        let mut ntk_rope = NtkRope::new(config, &device).unwrap();
+        
+        // This should fail in red phase
+        let result = ntk_rope.enable_fine_tuning_mode(4096);
+        assert!(result.is_err());
+    }
+    
+    // Test 11: Alpha parameter updates
+    #[test]
+    fn test_alpha_updates() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let config = NtkRopeConfig::new(128, 2048, 10000.0, 1024).unwrap();
+        let mut ntk_rope = NtkRope::new(config, &device).unwrap();
+        
+        // This should fail in red phase
+        let result = ntk_rope.update_alpha(3.0);
+        assert!(result.is_err());
+    }
+    
+    // Test 12: Layer trait implementation
+    #[test]
+    fn test_layer_trait() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let config = NtkRopeConfig::new(128, 2048, 10000.0, 1024).unwrap();
+        let ntk_rope = NtkRope::new(config, &device).unwrap();
+        
+        let input = Tensor::randn(&Shape::new(vec![2, 32, 4, 32]), &device).unwrap();
+        
+        // This should fail in red phase
+        let result = ntk_rope.forward(&input);
+        assert!(result.is_err());
+        
+        // Other trait methods should work
+        assert_eq!(ntk_rope.layer_type(), "NtkRope");
+        assert_eq!(ntk_rope.device(), &device);
+        assert_eq!(ntk_rope.parameters().len(), 0);
+    }
+    
+    // Test 13: Different scaling strategies
+    #[test]
+    fn test_scaling_strategies() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        
+        // Linear strategy
+        let config_linear = NtkRopeConfig::new(128, 2048, 10000.0, 1024)
+            .unwrap()
+            .with_strategy(ScalingStrategy::Linear);
+        let ntk_rope_linear = NtkRope::new(config_linear, &device).unwrap();
+        let (_, strategy) = ntk_rope_linear.get_scaling_params();
+        assert_eq!(strategy, ScalingStrategy::Linear);
+        
+        // Dynamic strategy
+        let config_dynamic = NtkRopeConfig::new(128, 2048, 10000.0, 1024)
+            .unwrap()
+            .with_strategy(ScalingStrategy::Dynamic);
+        let ntk_rope_dynamic = NtkRope::new(config_dynamic, &device).unwrap();
+        let (_, strategy) = ntk_rope_dynamic.get_scaling_params();
+        assert_eq!(strategy, ScalingStrategy::Dynamic);
+        
+        // YaRN strategy
+        let config_yarn = NtkRopeConfig::new(128, 2048, 10000.0, 1024)
+            .unwrap()
+            .with_strategy(ScalingStrategy::YaRN);
+        let ntk_rope_yarn = NtkRope::new(config_yarn, &device).unwrap();
+        let (_, strategy) = ntk_rope_yarn.get_scaling_params();
+        assert_eq!(strategy, ScalingStrategy::YaRN);
+    }
+    
+    // Test 14: Cache performance and management
+    #[test]
+    fn test_cache_management() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let config = NtkRopeConfig::new(128, 2048, 10000.0, 1024).unwrap();
+        let ntk_rope = NtkRope::new(config, &device).unwrap();
+        
+        // Check initial cache stats
+        let initial_stats = ntk_rope.cache_stats();
+        assert_eq!(initial_stats.entries, 0);
+        
+        // Pre-warm cache
+        let lengths = vec![512, 1024, 2048];
+        let result = ntk_rope.pre_warm_cache(&lengths);
+        assert!(result.is_ok());
+        
+        // Check memory estimation
+        let memory_usage = ntk_rope.estimate_memory_usage(1024);
+        assert!(memory_usage > 0.0);
+        
+        // Check sequence length support
+        assert!(ntk_rope.supports_length(1024));
+        assert!(!ntk_rope.supports_length(4096)); // Beyond max
+        
+        // Get optimal lengths
+        let optimal = ntk_rope.optimal_sequence_lengths();
+        assert!(!optimal.is_empty());
+        assert!(optimal.contains(&2048)); // Should include max length
+    }
+    
+    // Test 15: Configuration validation
+    #[test]
+    fn test_config_validation() {
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        
+        // Invalid dimensions
+        let result = NtkRope::new(
+            NtkRopeConfig::new(127, 2048, 10000.0, 1024).unwrap(), // Odd dimension
+            &device
+        );
+        assert!(result.is_err());
+        
+        // Zero dimension
+        let result = NtkRope::new(
+            NtkRopeConfig::new(0, 2048, 10000.0, 1024).unwrap(),
+            &device
+        );
+        assert!(result.is_err());
+        
+        // Zero max sequence length
+        let result = NtkRope::new(
+            NtkRopeConfig::new(128, 0, 10000.0, 1024).unwrap(),
+            &device
+        );
+        assert!(result.is_err());
+    }
+}